「ema」の検索結果

Audit YouTube Ads campaign setup, video and Demand Gen inventory, Shorts, in-stream, CTV, creative, audiences, brand safety, bidding, and measurement. Use for YouTube Ads, video ads, pre-roll, bumper ads, skippable in-stream, Shorts ads, Demand Gen, VAC migration, CTV, or YouTube campaign optimization.

日本語の概要は準備中です。原文の説明を表示しています。

SymPy is a Python library for symbolic mathematics that enables exact computation using mathematical symbols rather than numerical approximations.

日本語の概要は準備中です。原文の説明を表示しています。

Ensure event-driven designs specify delivery guarantees, ordering, idempotency, schema evolution, and replay/backfill strategy.

日本語の概要は準備中です。原文の説明を表示しています。

完成した差分・PR・検証証拠に残る Unknown（未確認の前提・調査されていない影響・ 不足している証拠）を横断合成する evidence-sufficiency のメタ観点。個別 defect の 検出は既存 skill へ委譲し、本 skill は「そのリスク種別を調査した証拠が残っているか」 の meta 評価のみを行う。通常は finding verification 後の generic 合成ステップとして、 明示的 Security Audit では SecurityAuditCoverage 専用 profile として report-only で実行する。 残存 Unknown は既存 Unknown Coverage 構造へ出力し、新しい語彙・schema は作らない。

Add redaction annotations to sensitive fields in schemas to block or hash PII. Use when configuring GDPR/HIPAA compliance, protecting credentials, or blocking internal fields from materializations. Use when user says "protect PII", "hide sensitive data", "hash field", "redact field", "GDPR compliance", "don't capture field", "remove field from Estuary", or "prevent Estuary from capturing field".

日本語の概要は準備中です。原文の説明を表示しています。

在授权渗透测试中系统性挖掘命令注入与参数/标志注入（OS command injection、argument/flag injection、外部程序 delegate 注入）。当目标把用户输入拼进 shell 命令、或作为参数传给 git/tar/curl/ffmpeg/ImageMagick 等外部程序、或经模板/eval 执行时使用——典型场景：文件名/路径/URL/ref 参数最终进了命令行，想拿 RCE 或任意文件读写。适用目标类型 Web / REST API / CI 系统。触发场景包括用户说"测下命令注入/RCE""这个参数会不会进 shell""git/tar/ffmpeg 参数能注入吗""上传头像触发 ImageMagick 了"。输出：带原始请求/命令回显或带外证据的 finding 报告（含 killed 记录）。

日本語の概要は準備中です。原文の説明を表示しています。

Implements type-safe reactive SQL persistence in Flutter using Drift v2.32 (formerly Moor) built on SQLite with automatic code generation. Activates when defining table schemas with Drift DSL, writing type-safe join or subquery operations, handling schema migrations with MigrationStrategy, using reactive watch() streams for live UI updates, implementing ACID transactions, defining foreign key constraints or cascade deletes, enabling WAL mode for write performance, troubleshooting generated .g.dart type mismatches, migrating from sqflite or sqlite3 v2 to v3, using DAOs for query separation, or building offline-first apps requiring strong relational data integrity and compile-time SQL verification.

日本語の概要は準備中です。原文の説明を表示しています。

Manage semantic version updates for rad-mem project. Handles patch, minor, and major version increments following semantic versioning. Updates package.json, marketplace.json, plugin.json, and CLAUDE.md version number (NOT version history). Creates git tags and GitHub releases. Auto-generates CHANGELOG.md from releases.

日本語の概要は準備中です。原文の説明を表示しています。

Design the MixerSceneIntent, approval state, JSON schema, CLI examples, audit entries, and tests for AI-Controlled Party mixer/Soundcraft Ui24R scene arming. Use whenever a task mentions mixer scenes, Ui24R snapshots/cues, operator-approved mixer control, show-director contract updates, schema updates, dry-run CLI shape, or follow-ups that revise this contract.

日本語の概要は準備中です。原文の説明を表示しています。

Email infrastructure for AI agents. Create accounts, send/receive emails, manage webhooks, and check karma balance via the AgentMail API.

日本語の概要は準備中です。原文の説明を表示しています。

Use when starting, stopping, or inspecting the dev stack for this Hydrolix Grafana datasource plugin — Grafana (with the plugin bind-mounted), ClickHouse, Keycloak (OAuth), and the on-demand playwright runner. Covers `docker compose up` semantics, the `dist/` prerequisite, serving plugin artifacts from an alternate folder (any path, live or frozen, via an ephemeral compose override), switching Grafana versions, hot-reload behaviour, delve attach, log streaming, OAuth/anonymous auth, and ClickHouse fixture access. Apply whenever a task needs the plugin running locally, needs to reset the stack, or needs to debug why Grafana won't come up.

日本語の概要は準備中です。原文の説明を表示しています。

Use when working with Cube Dev — cube.dev semantic layer management — monitor deployments, data models, pre-aggregations, query performance, and API health. Use when inspecting cube schemas, debugging slow queries, reviewing pre-aggregation builds, or auditing API usage.

日本語の概要は準備中です。原文の説明を表示しています。

Amazon demands Perplexity stop using AI agent to make purchases Amazon has sent a cease-and-desist

日本語の概要は準備中です。原文の説明を表示しています。

Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.

日本語の概要は準備中です。原文の説明を表示しています。

Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions.

日本語の概要は準備中です。原文の説明を表示しています。

Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.

日本語の概要は準備中です。原文の説明を表示しています。

Store and retrieve unstructured objects, file uploads, and cache-like state on Netlify using the @netlify/blobs key/value API from Functions, Edge Functions, and Build Plugins. Use when a task involves saving user file or image uploads, persisting form or contact-form submissions, storing generated output from Background Functions (sitemaps/processed media/bulk-email results), building read-only asset stores, adding client-side blob expiration, or wiring file-based blob uploads at deploy time. Not for per-user, transactional, or relational data (counters/balances/sessions) — reach for Netlify DB there instead.

日本語の概要は準備中です。原文の説明を表示しています。

Power BI semantic models - DAX measures, star schemas, relationships, RLS, and performance tuning via MCP. Use when creating data models, writing DAX, or configuring table relationships in Power BI.

日本語の概要は準備中です。原文の説明を表示しています。

Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.

日本語の概要は準備中です。原文の説明を表示しています。

Set up an isolated Python environment for SageMaker / AWS work, with the right Python version and current boto3. Use this skill whenever Python code will be executed for a SageMaker deployment, training job, or any AWS automation — including when about to run `pip install`, when about to invoke `boto3`, when creating or activating a virtualenv, or when the user asks to "set up the environment". Never use system Python and never `pip install` into it. Always isolate. This skill prevents the most common failure modes: wrong Python version, dependency conflicts, and stale SDKs.

日本語の概要は準備中です。原文の説明を表示しています。

Pick the right serving container for a SageMaker model deployment and find its current image URI. Use this skill whenever about to deploy a model to a SageMaker endpoint and an image URI needs to be chosen — including when the user says "deploy this LLM", "host this HuggingFace model", "serve this fine-tuned model", "deploy this embedding model", "host a reranker", "serve a sentence-transformers model", or when about to hardcode any container URI in deployment code. HuggingFace-curated Deep Learning Containers are ALWAYS preferred: HuggingFace vLLM (LLMs and generative rerankers), HuggingFace vLLM-Omni (multimodal), TEI (embeddings/cross-encoder rerankers), HF Inference Toolkit (other transformers). Generic images (AWS vLLM, DJL-LMI, SGLang) are used only when no HuggingFace image is compatible — never merely because they carry a newer version. Never hardcode a container URI from memory and never default to TGI. Prevents stale-image failures and wrong-region URIs.

日本語の概要は準備中です。原文の説明を表示しています。

Expert en cinématographie drone (DJI, planification de vol, réglementations, post-traitement, mapping)

日本語の概要は準備中です。原文の説明を表示しています。

Database architect v2 — schema design, normalization, denormalization, polyglot persistence, sharding

日本語の概要は準備中です。原文の説明を表示しています。

Copywriter IA — Expert en rédaction persuasive (landing pages, emails, ads, framework AIDA, psychologie de la persuasion).

日本語の概要は準備中です。原文の説明を表示しています。